





"""simple docstring"""






import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A			      =	get_logger(__name__)


A			      =	r'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''




class 					__lowercase      :







					'''simple docstring'''




					@add_start_docstrings(_UpperCAmelCase  )
					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									raise NotImplementedError(
									    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""  )






class 					__lowercase      :







					'''simple docstring'''




					@add_start_docstrings(_UpperCAmelCase  )
					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									raise NotImplementedError(
									    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""  )






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					@add_start_docstrings(_UpperCAmelCase  )
					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      **_UpperCAmelCase  ):
									for processor in self:
													__a			:    int      							=	inspect.signature(processor.__call__  ).parameters
													if len(_UpperCAmelCase  ) > 3:
																	if not all(arg in kwargs for arg in list(function_args.keys()  )[2:]  ):
																					raise ValueError(
																					    f"""Make sure that all the required parameters: {list(function_args.keys()  )} for """
																					    f"""{processor.__class__} are passed to the logits processor."""  )
																	__a			:    Optional[Any]      							=	processor(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      **_UpperCAmelCase  )
													else:
																	__a			:    Union[str, Any]      							=	processor(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase  ):
									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) or not (temperature > 0):
													raise ValueError(f"""`temperature` has to be a strictly positive float, but is {temperature}"""  )

									__a			:    int      							=	temperature







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Tuple      							=	scores / self.temperature
									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = -float('''Inf'''  )						,      _UpperCAmelCase = 1  ):
									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) or (top_p < 0 or top_p > 1.0):
													raise ValueError(f"""`top_p` has to be a float > 0 and < 1, but is {top_p}"""  )
									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) or (min_tokens_to_keep < 1):
													raise ValueError(f"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"""  )

									__a			:    List[str]      							=	top_p
									__a			:    str      							=	filter_value
									__a			:    List[str]      							=	min_tokens_to_keep







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a							,    __a			:    str      							=	lax.top_k(_UpperCAmelCase						,      scores.shape[-1]  )

									__a			:    Any      							=	jnp.full_like(_UpperCAmelCase						,      self.filter_value  )
									__a			:    List[Any]      							=	jax.nn.softmax(_UpperCAmelCase						,      axis=-1  ).cumsum(axis=-1  )
									__a			:    int      							=	cumulative_probs < self.top_p

									# include the token that is higher than top_p as well
									__a			:    Any      							=	jnp.roll(_UpperCAmelCase						,      1  )
									score_mask |= score_mask.at[:, 0].set(_UpperCAmelCase  )

									# min tokens to keep
									__a			:    List[str]      							=	score_mask.at[:, : self.min_tokens_to_keep].set(_UpperCAmelCase  )

									__a			:    Tuple      							=	jnp.where(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    Any      							=	jax.lax.sort_key_val(_UpperCAmelCase						,      _UpperCAmelCase  )[-1]

									return next_scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = -float('''Inf'''  )						,      _UpperCAmelCase = 1  ):
									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) or top_k <= 0:
													raise ValueError(f"""`top_k` has to be a strictly positive integer, but is {top_k}"""  )

									__a			:    Any      							=	max(_UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    int      							=	filter_value







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a							,    __a			:    Optional[int]      							=	scores.shape
									__a			:    Optional[Any]      							=	jnp.full(batch_size * vocab_size						,      self.filter_value  )

									__a			:    Any      							=	min(self.top_k						,      scores.shape[-1]  )  # Safety check
									__a							,    __a			:    str      							=	lax.top_k(_UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    Tuple      							=	jnp.broadcast_to((jnp.arange(_UpperCAmelCase  ) * vocab_size)[:, None]						,      (batch_size, topk)  ).flatten()
									__a			:    Dict      							=	topk_scores.flatten()
									__a			:    Optional[Any]      							=	topk_indices.flatten() + shift

									__a			:    Tuple      							=	next_scores_flat.at[topk_indices_flat].set(_UpperCAmelCase  )
									__a			:    Any      							=	next_scores_flat.reshape(_UpperCAmelCase						,      _UpperCAmelCase  )
									return next_scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase  ):
									__a			:    int      							=	bos_token_id







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Dict      							=	jnp.full(scores.shape						,      -float('''inf'''  )  )

									__a			:    str      							=	1 - jnp.bool_(cur_len - 1  )

									__a			:    Tuple      							=	jnp.where(_UpperCAmelCase						,      new_scores.at[:, self.bos_token_id].set(0  )						,      _UpperCAmelCase  )

									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    List[Any]      							=	max_length
									__a			:    Optional[int]      							=	eos_token_id







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    List[Any]      							=	jnp.full(scores.shape						,      -float('''inf'''  )  )

									__a			:    Optional[int]      							=	1 - jnp.bool_(cur_len - self.max_length + 1  )

									__a			:    Tuple      							=	jnp.where(_UpperCAmelCase						,      new_scores.at[:, self.eos_token_id].set(0  )						,      _UpperCAmelCase  )

									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) or min_length < 0:
													raise ValueError(f"""`min_length` has to be a positive integer, but is {min_length}"""  )

									if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) or eos_token_id < 0:
													raise ValueError(f"""`eos_token_id` has to be a positive integer, but is {eos_token_id}"""  )

									__a			:    Union[str, Any]      							=	min_length
									__a			:    int      							=	eos_token_id







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									# create boolean flag to decide if min length penalty should be applied
									__a			:    Dict      							=	1 - jnp.clip(cur_len - self.min_length						,      0						,      1  )

									__a			:    Dict      							=	jnp.where(_UpperCAmelCase						,      scores.at[:, self.eos_token_id].set(-float('''inf'''  )  )						,      _UpperCAmelCase  )

									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    int      							=	list(_UpperCAmelCase  )
									__a			:    List[Any]      							=	begin_index







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    List[Any]      							=	1 - jnp.bool_(cur_len - self.begin_index  )

									__a			:    int      							=	jnp.where(_UpperCAmelCase						,      scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''  )  )						,      _UpperCAmelCase  )

									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase  ):
									__a			:    Union[str, Any]      							=	list(_UpperCAmelCase  )







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Dict      							=	scores.at[..., self.suppress_tokens].set(-float('''inf'''  )  )

									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase  ):
									__a			:    Optional[int]      							=	dict(_UpperCAmelCase  )
									# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
									# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
									# Indexes without forced tokens will have a negative value.
									__a			:    Optional[int]      							=	jnp.ones((max(force_token_map.keys()  ) + 1)						,      dtype=jnp.intaa  ) * -1
									for index, token in force_token_map.items():
													if token is not None:
																	__a			:    Optional[int]      							=	force_token_array.at[index].set(_UpperCAmelCase  )
									__a			:    List[Any]      							=	jnp.intaa(_UpperCAmelCase  )







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									def _force_token(_UpperCAmelCase  ):
													__a			:    Tuple      							=	scores.shape[0]
													__a			:    Tuple      							=	self.force_token_array[generation_idx]

													__a			:    int      							=	jnp.ones_like(_UpperCAmelCase						,      dtype=scores.dtype  ) * -float('''inf'''  )
													__a			:    int      							=	jnp.zeros((batch_size, 1)						,      dtype=scores.dtype  )
													__a			:    Dict      							=	lax.dynamic_update_slice(_UpperCAmelCase						,      _UpperCAmelCase						,      (0, current_token)  )
													return new_scores

									__a			:    Union[str, Any]      							=	lax.cond(
									    cur_len >= self.force_token_array.shape[0]						,      lambda: scores						,      lambda: lax.cond(
									        self.force_token_array[cur_len] >= 0						,      lambda: _force_token(_UpperCAmelCase  )						,      lambda: scores						,      )						,      )
									return scores






class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Union[str, Any]      							=	generate_config.eos_token_id
									__a			:    Dict      							=	generate_config.no_timestamps_token_id
									__a			:    Optional[int]      							=	generate_config.no_timestamps_token_id + 1

									__a			:    List[Any]      							=	decoder_input_length + 1

									if generate_config.is_multilingual:
													# room for language token and task token
													self.begin_index += 2
									if hasattr(_UpperCAmelCase						,      '''max_initial_timestamp_index'''  ):
													__a			:    List[Any]      							=	generate_config.max_initial_timestamp_index
									else:
													__a			:    List[str]      							=	model_config.vocab_size
									if self.max_initial_timestamp_index is None:
													__a			:    Tuple      							=	model_config.vocab_size







					def __call__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									# suppress <|notimestamps|> which is handled by without_timestamps
									__a			:    int      							=	scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''  )  )

									def handle_pairs(_UpperCAmelCase						,      _UpperCAmelCase  ):
													__a			:    Any      							=	jnp.where((cur_len - self.begin_index) >= 1						,      _UpperCAmelCase						,      _UpperCAmelCase  )
													__a			:    int      							=	jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin						,      True and last_was_timestamp						,      _UpperCAmelCase						,      )

													__a			:    List[Any]      							=	jnp.where((cur_len - self.begin_index) < 2						,      _UpperCAmelCase						,      _UpperCAmelCase  )
													__a			:    Optional[Any]      							=	jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin						,      _UpperCAmelCase						,      _UpperCAmelCase						,      )

													return jnp.where(
													    _UpperCAmelCase						,      jnp.where(
													        penultimate_was_timestamp > 0						,      scores_k.at[self.timestamp_begin :].set(-float('''inf'''  )  )						,      scores_k.at[: self.eos_token_id].set(-float('''inf'''  )  )						,      )						,      _UpperCAmelCase						,      )

									__a			:    Optional[int]      							=	jax.vmap(_UpperCAmelCase  )(_UpperCAmelCase						,      _UpperCAmelCase  )

									__a			:    int      							=	jnp.where(cur_len == self.begin_index						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    Union[str, Any]      							=	jnp.where(
									    self.max_initial_timestamp_index is not None						,      True and apply_max_initial_timestamp						,      _UpperCAmelCase						,      )

									__a			:    List[str]      							=	self.timestamp_begin + self.max_initial_timestamp_index

									__a			:    Optional[int]      							=	jnp.where(
									    _UpperCAmelCase						,      scores.at[:, last_allowed + 1 :].set(-float('''inf'''  )  )						,      _UpperCAmelCase						,      )

									# if sum of probability over timestamps is above any other token, sample timestamp
									__a			:    Optional[Any]      							=	jax.nn.log_softmax(_UpperCAmelCase						,      axis=-1  )

									def handle_cumulative_probs(_UpperCAmelCase						,      _UpperCAmelCase  ):
													__a			:    Any      							=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]						,      axis=-1  )
													__a			:    Tuple      							=	jnp.max(logprobs_k[: self.timestamp_begin]  )
													return jnp.where(
													    timestamp_logprob > max_text_token_logprob						,      scores_k.at[: self.timestamp_begin].set(-float('''inf'''  )  )						,      _UpperCAmelCase						,      )

									__a			:    Dict      							=	jax.vmap(_UpperCAmelCase  )(_UpperCAmelCase						,      _UpperCAmelCase  )

									return scores


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


